1. Field of the Invention
The invention generally relates to systems, methods, and computer program products for providing real-time information to a user of a securities trading system. More particularly, aspects of the invention relate to providing one or more widgets that can display real-time graphical representations of analytics including real-time market information and intraday analysis of trade executions.
2. Description of the Background
Pre-trade planning has often been overlooked by portfolio managers and traders in the past, but in recent years it has become an important component of a superior investment process. The pre-trade planning phase can serve as a link between portfolio managers and traders where traders can share their insight and knowledge of the complicated trading environment with portfolio managers while portfolio managers can incorporate this additional insight and knowledge into their investment decisions. Additional information about the trading environment can be advantageous for an institutional trader or an investment manager, because execution costs can be large when compared against gross returns. A significant portion of these execution costs can be the price impact of trading (i.e., the effect of trades on the price of securities). For example, large “buy” orders can cause the price of the stock to rise, making the completion of these orders more costly to the investor. Similarly, large “sell” orders can cause the price of a stock to fall, making the completion of these orders less profitable to the trader. This realization as well as additional fragmentation in the markets and changes in regulation have increased the popularity of pre-trade models amongst traders to minimize or avoid the price impact of trading.
Once a portfolio manager has made the assumptions and analysis necessary to determine asset allocations and create a portfolio of securities, it is important that he or she convey those underlying assumptions to the trading desk or the trader who is about to execute the orders. Making clear the time frame in which the portfolio manager expects to accomplish the program and the cost estimates the manager expects can save all parties significant expense.
Even after gathering the underlying assumptions, traders still have substantial leeway in planning their execution strategies. With careful pre-trade strategy planning, a trader's benchmark, in many ways, becomes the cost profile a portfolio manager expects to achieve. The trader can then consider different trading strategies that will fit or “beat” the estimated cost benchmark that was agreed.
When planning an execution strategy, traders typically consider the relationship between price impact and opportunity cost (the risk that an advantageous opportunity will be lost by delaying a trade). An aggressive strategy (i.e., executing more of the trade in a shorter time frame) will typically increase price impact and lower opportunity cost. A passive strategy (i.e., delaying the execution of a greater portion of the trade), by contrast, will reduce price impact but increase opportunity cost. A selected execution strategy will often take into account both the estimated costs of a portfolio manager's investment strategy and the price impact and opportunity costs of executing that strategy.
Current pre-trade analysis services typically provide a static view of estimated transaction costs. For example, traditional pre-trade reporting focuses on delivering an abundance of information, typically in a PDF or HTML “report” format, that can be used to help with strategy selection and ex-post trading performance analysis. However, the relatively static nature of the reports limits their usefulness during actual trading. Because these services are external to the portfolio manager's workflow, there can be a substantial delay between submitting the trade list and performing the recommended executions.
In addition, current systems typically omit what the inventors consider to be a critical piece of monitoring information: deviations from history and expectations as the reality of the trading day evolves. Performance will only match expectations if all the assumptions underlying a certain strategy hold true over the course of the entire trading day. Unforeseen events that occur in the course of the trading day can significantly alter the overall performance of execution. A volume-distribution strategy, for example, will be sensitive to news that alters the expected volume distribution underlying the automated strategy. News released in the midst of the trading day that produces a sudden spike in volume could cause disastrous results if the trader fails to modify the strategy to adapt to events as they happen. Once the trading day begins, traders need to work dynamically, changing strategy “on the fly” to meet events (e.g., to quickly identify underperforming orders, to determine what mid-course corrections are possible, etc.).